- Python Text Processing - Home
- Python Text Processing - Introduction
- Python Text Processing - Environment
- Python Text Processing - String Immutability
- Python Text Processing - Sorting Lines
- Python Text Processing - Counting Token in Paragraphs
- Python Text Processing - Binary ASCII Conversion
- Python Text Processing - Strings as Files
- Python Text Processing - Backward File Reading
- Python Text Processing - Filter Duplicate Words
- Python Text Processing - Extract Emails from Text
- Python Text Processing - Extract URL from Text
- Python Text Processing - Pretty Print
- Python Text Processing - State Machine
- Python Text Processing - Capitalize and Translate
- Python Text Processing - Tokenization
- Python Text Processing - Remove Stopwords
- Python Text Processing - Synonyms and Antonyms
- Python Text Processing - Translation
- Python Text Processing - Word Replacement
- Python Text Processing - Spelling Check
- Python Text Processing - WordNet Interface
- Python Text Processing - Corpora Access
- Python Text Processing - Tagging Words
- Python Text Processing - Chunks and Chinks
- Python Text Processing - Chunk Classification
- Python Text Processing - Classification
- Python Text Processing - Bigrams
- Python Text Processing - Process PDF
- Python Text Processing - Process Word Document
- Python Text Processing - Reading RSS feed
- Python Text Processing - Sentiment Analysis
- Python Text Processing - Search and Match
- Python Text Processing - Text Munging
- Python Text Processing - Text wrapping
- Python Text Processing - Frequency Distribution
- Python Text Processing - Summarization
- Python Text Processing - Stemming Algorithms
- Python Text Processing - Constrained Search
Python Text Processing Useful Resources
Python Text Processing - Extract Emails from Text
To extract emails form text, we can take of regular expression. In the below example we take help of the regular expression package to define the pattern of an email ID and then use the findall() function to retrieve those text which match this pattern.
Example - Extracting Email
main.py
import re
text = "Please contact us at contact@tutorialspoint.com for further information." + \
" You can also give feedbacl at feedback@tp.com"
emails = re.findall(r"[a-z0-9\.\-+_]+@[a-z0-9\.\-+_]+\.[a-z]+", text)
print(emails)
Output
When we run the above program, we get the following output −
['contact@tutorialspoint.com', 'feedback@tp.com']
Advertisements